A38 Characterization of inclusions combined with arc … · Characterization of inclusions combined with arc/spark OES spectrochemical analysis ... Addition of Ca-Si alloy changes

Abstract No. 38 Characterization of inclusions combined with arc/spark OES spectrochemical analysis – Optimal quality and process control in seconds

Dr. Jean-Marc Böhlen*, Dr. Kaizhen Li*, and Dr. Ravi Yellepeddi*

Thermo Fisher Scientific, Ecublens, Switzerland

Abstract

Non-metallic inclusions are of considerable importance for the steel industry due to the dramatic influence that even small amounts of them may have on physico-chemical properties of the metal and on the production process itself. The inclusions can have positive effects that increase the value of the steel, but most often inclusions signify quality problems and additional costs. This is true in particular for an increasing number of steel types used for demanding applications and industries (e.g. automotive, aerospace, nuclear, offshore and pipeline industries). Therefore for many companies, for many steel grades and products, a tighter control of the critical non-metallic micro-inclusions during production process is essential to guarantee steel cleanness and properties, and to avoid considerable quality costs and process issues.

Optical Emission Spectrometry (OES) is routinely used as a production process control tool in every steel plant and places itself ideally suited to achieve more advanced analysis of inclusions faster than any other method. The Thermo Scientific™ ARL iSpark™ allows qualitative evaluation of inclusions in terms oftype, composition, size (distribution) and number. In advanced methods, this instrument also allows quantitative size (distribution) analysis and in killed steels determination of oxygen concentration down to a few ppm. It achieves inclusion and elemental spectrochemical analyses together in seconds, making ARL iSpark the all-in-one instrument for optimal, economical, in-process steel quality control.

In the previous edition of the CETAS Conference, the principles were presented and illustrated with examples of the above-mentioned methods. The latest advances, applications and reflexions in the analysis of non-metallic micro-inclusions are exposed in the present edition.

Biography

Dr. Kaizhen Li holds a Master’s degree in environmental engineering and a Ph.D. in Chemistry with specialization in determination of trace element in environment by mass spectroscopy. She is currently application specialist of Optical Emission Spectrometers at Thermo Fisher Scientific.CE

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Abstract No. 38 Dr. Jean-Marc Böhlen holds a Master’s degree in Chemistry and a Ph.D. in NMR Spectroscopy from the University of Lausanne. He is currently Product Manager in charge of the Optical Emission Spectrometers at Thermo Fisher Scientific.Dr. Ravi Yellepeddi holds a Master’s degree in Physics and a Ph.D. in Physics-Solid State Spectroscopy. He is currently the Director of Marketing and Business Development at Thermo Fisher Scientific.

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Optimal quality and process control in seconds

J.-M. Böhlen and K. Li Thermo Fisher Scientific, CH-1024 Ecublens

CETAS 2015 9th International Conference on Progress in Analytical Chemistry & Materials Characterisation in the Steel and Metals Industries Düsseldorf, May 19-21, 2015

R. Dekkers et al., Metallurgical and Materials Transactions B, Vol. 24BB, 2003, No. 2, 161-171

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Ultra-fast inclusion analysis by OES

• OES (Optical Emission Spectroscopy)

instruments have been used for decades for quantitative elemental analysis in process control of most steel and metallurgical plants

• Since 20 years, Thermo Scientific OES instruments have also been used for the evaluation of non-metallic micro-inclusions • First with the ARL 4460 and now with the ARL iSpark

• Technology and methods are called “Spark-DAT”

ARL iSpark

ARL 4460

The main attributes of Spark-DAT are its speed and the fact that it is performed during the OES spectrochemical analysis

Gives a tool to the metallurgical plants to have quick information on inclusions in production (not available quickly with methods like SEM/EDX) CE

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis

Optimal quality and process control in seconds • As presented in the last edition of the CETAS Conference, Spark-DAT allows

“basic” evaluation of inclusions (qualitative or relative), e.g. • Number of inclusions by type

• Relative “SML” sizes (Small, Medium, Large) or size distribution by type

• With the latest methods it also allows quantitative determinations • Number of inclusions by type

• Concentration of inclusions by type

• Size and size distribution of inclusions by type

• Determination of oxygen concentration down to a few ppm’s in killed steels

• Spark-DAT can be involved in different contexts • Control of the production process - Right inclusions at right process stage !

• Problem solving / improvement of the production process, of the raw materials and of the metal quality

• Fast sample screening - R&D, problem solving, ...

• Control of inclusions in finished or semi-finished products

• ...

Different types of inclusion information beneficial in different contexts

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Ca

Al

+ other element channels

spark number


Principles - Reminder

• High frequency single sparks hitting a steel sample

baseline intensity =

Signal from the low concentration Ca atoms in solution in the matrix

Ca intensity peak =

Signal from the high concentration Ca atoms in an inclusion

Coincident Ca and Al peaks (correlation) Ca and Al in the same inclusion (e.g. Ca aluminate)

Ca peak not coincident with Al peak (uncorrelation) Inclusion contains Ca but not Al (e.g. Ca oxide)

Statistical evaluation of intensity peaks, of their coincidences and of their intensity is the basis of OES inclusion analyses

The intensity of the peak is proportional to the volume of the inclusion ! CETA

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Principles - Spark-DAT methods

•Spark-DAT algorithms • Insoluble: determination of the soluble/insoluble fractions of Al, B, Ca, Ti,…

• Peaks: evaluation of number of inclusions by element and size classes

• Composition: evaluation of the number of inclusions by type and size classes

• QuIC: Quantitative determination of the size of inclusions by type

Quantitative determination of O concentration down to a few ppm’s … and more

Selected inclusion information

Complex intensity data

2’000 values for each element channel

Fast algorithms

•Spark-DAT methods involve fast processing algorithms to generate the useful inclusion information

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• Basic qualitative peaks and coincidental peaks count in two low alloy steel samples (same grade) from two different castings

• Sample 1 • Mainly Ca oxides and sulfides

• Sample 2 • Mainly Ca aluminates and Al oxides


Quick Spark-DAT numbers to see the difference

Al tot O tot Ca tot S totSample 1 60 87 192 61Sample 2 132 78 117 11

Al2O3 Ca alum. CaO CaSSample 1 0 8 17 32Sample 2 9 14 4 4

Peaks

Composition

(average of 5 runs)

While the samples are almost identical chemically (i.e. for the standard OES), Spark-DAT sees what makes them very different:

their inclusion contents

Al

O

Ca

S

Sample 1 Sample 2

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Optimal process control via on-line monitoring of inclusions

Al2O3

MgO·Al2O3

Al2O3·CaO

CaS

MgO·Al2O3

Al2O3·CaO

Inclusions morphology control at continuous casting Al2O3 inclusions generated after Al addition crystallize and cause clogging in the nozzle

Addition of Ca-Si alloy changes inclusions composition

• Al2O3 Al2O3·CaO

With globularAl2O3·CaO inclusions, clogging is reduced

Excess of Ca-Si further modifies the inclusions (CaS), which lowers the steel fluidity and causes casting problems

(Example with permission of Stanislav Zielina, Trinec Iron & Steel Works , CZ)

Monitoring of Al2O3, Al2O3·CaO and CaS with Spark-DAT for process control allows quick adjustments of the Ca-Si additions, extending nozzle lifetime, reducing additive costs and quality problems

Simple with SPC charts !

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• Spark-DAT results sent to SPC like OES concentrations

Characterization of inclusions combined with arc/spark OES spectrochemical analysis Optimal process control - Use SPC charts ! (example in Al)

B

Na

Mg

Ti K O

Mean

Range

Xbar & Range chart Sample #5

Sample #14

Configured warnings for samples / inclusions out of the defined control limits CE

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Effect of scrap quality on inclusion content of final product

0

20

40

60

80

100

0,5 0,7 0,9 1,1 1,3 1,5 1,7 1,9

Nu

mb

er

ESD (µm)

Al2O3 content in final casting samples heats using cleaner scraps heats using uncontroled scraps

Less inclusions and almost only the smallest ones in the sample from heat made of cleaner scrap

• Size analysis (3 size classes defined) of Al oxides in two samples from the same casting process, but made out of different scrap qualities

SiMn-killed steel with Al wire treatment at the final casting stage

Correlations with various process parameters – here the scrap quality – help understanding and improving process and product quality

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Replace Baumann sulfur print with Spark-DAT

• Like the Bauman test, Spark-DAT shows more sulfides in the middle of the plate

S peaks and coincident MnS peaks counted by Spark-DAT

• Macrographic examination by contact printing using silver salts and sulphuric acid to reveal the distribution of sulfide inclusions

(ISO 4968:1979, ASTM E1180 - 08 )

S MnS S MnS

Top 138 9 136 8

Centre 323 25 345 31

Bottom 122 2 100 6

Position A Position BPlate height

Top

Bottom

Steel plate

Spark-DAT is however faster and also provides information on other inclusions types

It advantageously replaces other inclusion methods ! CE

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Qualitative SML size analysis and size distribution

• More classes possible, e.g. to control size distributions of castings

•Easy differentiation of small, medium and large inclusions

• Large inclusions are normally more detrimental to quality!

small medium large

Ca in a low alloy steel Vertical axis: nb of inclusions/mm3

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Quantitative inclusion size (distribution) determination

ESD: Equivalent Spherical Diameter

•Quantitative inclusion size and distribution is obtained with a mixed use of the algorithm QuIC (Quantitative Inclusion Contents) and calculation

Size distributions of Al2O3 in 3 low alloy steel samples

ESD [ m]

For each size class, we calculate the average mass and from there the average size (ESD) of the inclusions, assuming that they are spherical

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Quantitative inclusion size determination - Examples

• Average inclusion size in 4 steel samples • 3 size classes

defined

Size evaluated in seconds !

Potentially very accurate, since based on inclusion volume

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Inclusion concentration • Concentration, size and number of different inclusion types evaluated with

Spark-DAT in 4 low alloy steel samples (same grade with ~3%Cr, 0.5%Mo, 0.1%Mn)

• Sample 1 has excess of Al, Ca, Mg, Si and Ti based oxides

• Sample 2 has excess of Mg based oxides

• Samples 3 and 4 are good

Sizeclass

Conc. (ppm)

ESD ( m)

NbConc. (ppm)

ESD ( m)

NbConc. (ppm)

ESD ( m)

NbConc. (ppm)

ESD ( m)

Nb

L 1.9 3 - - 2.1 1 1.7 2M 1.4 7 - - 1.3 2 1.2 1S 0.9 23 0.6 3 0.8 9 0.7 8L - - - - - - - - M 0.7 1 0.3 1 0.5 1 0.6 1S 0.7 13 0.3 4 0.5 11 0.6 14L 0.7 1 0.8 16 - - - - M 1.0 2 0.6 7 0.6 2 - - S 0.5 11 0.4 23 0.3 2 0.2 4L - - - - - - 6.7 1M - - - - - - - - S 2.3 1 1.4 2 1.3 1 1.4 1L 5.5 5 6.2 1 6.3 1 6.3 1M 4.3 4 - - - - 3.7 1S 1.9 15 3.2 - 2.7 1 1.8 1L 1.6 7 - - 1.9 1 1.3 1M 1.1 8 1.1 1 - - 0.9 1S 0.5 19 0.4 3 0.5 4 0.5 4L - - - - - - - - M - - - - - - - - S 0.9 1 - - - - - - L - - - - - - - - M - - - - - - - - S 1.4 6 0.8 6 1.4 8 1.3 14

0.17

CaS 0.006 - - -

MnS 0.12 0.03 0.26

0.03

SiO2 9.44 0.56 1.2 1.58

TiO2 0.46 0.01 0.02

0.02

0.04

MgO 0.03 0.11 0.007 0.004

MnO 0.19 0.05 1.22

0.01

Al2O3 0.6 0.01 0.13 0.18

CaO 0.04 0.005

Sample 1 Sample 2 Sample 3 Sample 4

Concentration, size and number measured on production samples are compared to reference or quality target values CE

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Sample 3 Sample 4

Good samples


Ternary diagrams

•Spark-DAT inclusion data can also be used to build up ternary diagrams

Sample 1 Sample 2

Bad samples

Spark-DAT based ternary diagrams

High Al, Ca and Mg based oxides

High Mg based oxides

•Same 4 low alloy steel samples as in previous slide

(with ~3%Cr, 0.5%Mo, 0.1%Mn)

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On-line monitoring of inclusions along the process

• Too many oxides at start of casting

• Large amount of Al based inclusions observed after addition of Al that end up in the billet

• Ferro alloys (FeMn, FeSi ) introduced in ladle furnace • Al added during casting

Si Mn Al Ca Mg O S

11st from ladle furnace

0.19 0.50 0.0033 0.0006 0.0001 0.017 0.025

2Last from ladle furnace

0.22 0.79 0.0019 0.0013 0.0002 0.008 0.024

3Start of casting

0.22 0.78 0.0183 0.0011 0.0001 0.0174 0.025

4Middle of casting

0.22 0.79 0.0157 0.0011 0.0001 <0.005 0.025

5End of casting

0.21 0.78 0.0014 0.0009 0.0001 <0.005 0.024

6From billet after end of casting

0.19 0.65 0.0140 0.0007 0.0002 <0.005 0.027

Inclusion concentrationProcess step

Sample

• Heat information:

Problems can be quickly identified during production of steel CETA

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Total oxygen content

• Total oxygen content can be calculated from the oxygen contained in oxides • Number and size of the various oxides oxygen content (O channel not used)

Killed-steels treated with typical deoxidizers (Al, FeSi, FeMn, Ca…)

(samples from continuous casting mould and combustion results with permission of R. Dumarey and F. Medina, ArcelorMittal, Ghent)

Certified reference carbon & low alloy steels (IARM)

Accuracy & precision competitive with com-bustion analyzers at very low concentrations

Low investment and operating cost, no skill needed !

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Practical details and limitations of Spark-DAT methods

• Sample preparation • Grinding or milling for qualitative methods for ferrous metals

• Milling for non-ferrous metals and for quantitative methods with steel

• Time of analysis • About 7s for a single measurement in stand-alone mode (qualitative methods only)

• About 20s for a single measurement combined with OES elemental analysis • < 1 minute, in the typical practice of steel plants (average of 2 measurements)

• Suitable for many types of inclusions

• Suitable for inclusions in other metal matrices (Al, Cu, Ni, Pb, etc.)

• Limitations • Sensitivity - as per the OES limits of detection

• e.g. higher for Ca, Al, S, Ti, etc and lower for N and O

• Smallest detectable size depends on inclusion type and matrix composition

• Typically from less than 1mm to a few mm CETA

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Spark-DAT vs. microscopy

• Correlation between microscopy and Spark-DAT not possible with some samples • Can be due to different limitations of the two techniques

Type D oxides determined by microscopic method (ASTM E45 method D)

? ?

Spark-DATSurface

techniquesTruth

1 1 1

1 0 1

1 2 2

1 1 2

1 0 1

Inclusions counted

Surface analysis sees inclusions smaller than they are!

The two techniques perceive inclusions differently!

OES and microscopy perceive inclusions differently.

Question: “Is the replacement of standard methods the best possible use of Spark-DAT ?”

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Characterization of inclusions combined with arc/spark OES spectrochemical analysis Conclusion

• OES is a very well established analysis technique in the metals industry, and Spark-DAT extends its capability much beyond elements determination

• Its speed makes Spark-DAT a unique tool for the control of inclusions at different stages of the production process • It is nowadays performed by many companies on every sample analyzed by OES

• Key benefits • Drastic reduction of investment costs (“All-in-One” analyzer)

• Inclusion analysis is performed during the standard OES analysis • Same high sample throughput as standard spark-OES

• Offers unequalled perspectives for controlling inclusions during steel production • Simple and quick sample preparation

• No additional cost and time compared to standard OES operations, for maintenance and service and no specific standardization needed

• Quantitative analysis of oxygen at concentrations of 30ppm and lower in killed steels • Usage of costly combustion analyzers can be reduced

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For information on OES product range www.thermoscientific.com/oes ARL iSpark Series OES spectrometers www.thermoscientific.com/ispark Inclusion analysis in Steel www.thermoscientific.com/sparkdat-fe Inclusion analysis in Aluminum www.thermoscientific.com/sparkdat-al

Visit www.thermofisher.com for information on our other Laboratory and Online Analysis solutions: Cross-Belt Analyzers X-Ray Fluorescence (XRF) X-Ray Diffraction

To be informed on our latest developments and special offers, subscribe to our e-newsletter MATERIALS ANALYST www.thermoscientific.com/siregister

Don’t wait to evaluate Spark-DAT, contact our local sales office !

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A different view on non-metallic inclusions

Funny !

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Animal !

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Majestic !

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Fairy !

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Archeological !

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Family !

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Pictures with permission of Test Center of Baosteel Research Institute

German !

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Documents

A38 Characterization of inclusions combined with arc … · Characterization of inclusions combined with arc/spark OES spectrochemical analysis ... Addition of Ca-Si alloy changes